SARA Water Quality Modeling Tool Development
Developing software tools to facilitate watershed modeling and planning in San Antonio River Authority’s (SARA) watersheds.
In recent years, selecting and implementing best management practices (BMPs) and low-impact development (LID) strategies to address urban runoff pollution have become important components of holistic watershed master planning and stormwater management. However, these strategies have often been limited to qualitative planning because suitable tools are lacking to conduct a quantitative assessment. As a result, the effectiveness of alternative BMP/LID practices could only be discerned by follow-up, long-term monitoring. To help address this limitation and support compliance with increasing water quality regulations, the San Antonio River Authority (SARA) funded the development of several innovative water quality modeling tools to allow quantitative water quality master planning and BMP/LID prioritization for three major watersheds in the San Antonio River Basin.
In support of the SARA holistic watershed master planning, RESPEC helped develop a set of water quality modeling tools. The SARA Load Reduction Tool automatically determines load reduction needed for each subbasin and constituent to meet user-specified water quality concentrations. The SARA BMP Processor compiles individual BMP/LID unit-cost and effectiveness information to assess potential incentives for implementing BMPs/LIDs. The SARA Enhanced BMP Tool determines the optimal combinations that would minimize the BMP/LID costs while achieving the needed load reduction. This tool combines robust land-surface representation from HSPF with the US Environmental Protection Agency’s (EPA) SUSTAIN model BMP/LID simulation and optimization capabilities but uses only the non-GIS engine (SUSTAINOPT) to run in a stand-alone mode.
The BMP Tool also included a compilation of available BMP/LID data and applied engineering economic analyses to convert the collected data to annual costs for equal-footing comparison and optimization. The data were stored in a comprehensive BMP Tool Database used by the BMP Tool when optimizing BMP/LID types and numbers. The results from SUSTAINOPT were then fed back into the HSPF model to verify that the preferred load reductions were met. The project earned an American Council of Engineering Companies (ACEC) National Recognition Award in the 2016 Engineering Excellence Awards competition—the “Academy Awards of the engineering industry.” The National Recognition Award is a prestigious distinction to honor projects that demonstrate exceptional achievement in engineering.
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